code: def load_t5(model_name, device="cuda"):
      tokenizer = T5Tokenizer.from_pretrained(model_name)
      model = T5ForConditionalGeneration.from_pretrained(
          model_name, device_map="auto"
      )
      model = model.to(device)
      model_wrapped = HF_LM(model, tokenizer, device=device)
      return model_wrapped
  
file_path: interpret-text/python/interpret_text/generative/model_lib/models.py
imports: import torch
  from transformers import AutoTokenizer, AutoModelForCausalLM, T5Tokenizer, T5ForConditionalGeneration
  from transformers import LlamaTokenizer, LlamaForCausalLM
  from openai_tooling import CompletionsOpenAI, ChatOpenAI
  from hf_tooling import HF_LM
  from accelerate import Accelerator
label_desc: Use image node_id 2 for calling a global function with example usage: load_t5(model_name, device) and returns: model_wrapped